{"id":4871,"date":"2023-10-20T06:28:14","date_gmt":"2023-10-20T06:28:14","guid":{"rendered":"https:\/\/bahria.edu.pk\/coeai\/?p=4871"},"modified":"2023-10-31T06:37:17","modified_gmt":"2023-10-31T06:37:17","slug":"deep-fake-video-detection","status":"publish","type":"post","link":"https:\/\/coeai.bahria.edu.pk\/index.php\/deep-fake-video-detection\/","title":{"rendered":"Deep Fake Video Detection"},"content":{"rendered":"\t\t
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Deep Fake Video Detection<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Th<\/span>is project<\/span> is aimed at early prediction of Parkinson\u2019s disease through computerized analysis of online handwriting samples. While <\/span>a number of<\/span> clinical evaluations are carried out to diagnose the Parkinson\u2019s disease, most of these tests are effective only once the disease is at a relatively advanced stage. Studies have shown that analysis of handwriting can be used as a valuable tool for prediction of Parkinson\u2019s disease at very early stages. In the proposed research, we have employed raw signals captured by a digitizer tablet while attempting several writing and drawing templates, to compute various kinematic, temporal, and pen-based attributes. In addition to these online dynamic attributes, static offline visual features are also extracted using state-of-the-art Convolutional Neural Networks (CNNs). <\/span>The developed tools allow <\/span>practitioner<\/span>s<\/span> to collect handwriting and drawing samples of subjects<\/span> and<\/span> employ a combination of different types of features (online and offline) for the analysis and prediction.<\/span><\/span>\u00a0<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Developed a Deep Fake Video Classification application that can identify deepfake videos. This application allows users to upload a video and provides results indicating whether the video is real or Fake.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Project Team<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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  • Dr. Sumaira Kausar<\/li>\n<\/ul><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t
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    • MUHAMMAD KHIZER ALI<\/li>\n<\/ul><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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      Publications<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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      1. Moetesum, M., Diaz, M., Masroor, U., Siddiqi, I., & Vessio, G. (2022). A survey of visual and procedural handwriting analysis for neuropsychological assessment.\u202f<\/span>Neural Computing and Applications<\/span><\/i>, 1-18.<\/span><\/li>
      2. Diaz, M., Moetesum, M., Siddiqi, I., & Vessio, G. (2021). Sequence-based dynamic handwriting analysis for Parkinson\u2019s disease detection with one-dimensional convolutions and BiGRUs. Expert Systems with Applications, 168, 114405.<\/span><\/li>
      3. Moetesum, M., Siddiqi, I., Javed, F., & Masroor, U. (2020, September). Dynamic Handwriting Analysis for Parkinson’s Disease Identification using C-BiGRU Model. In\u202f<\/span>2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR)<\/span><\/i>\u202f(pp. 115-120). IEEE.<\/span><\/li><\/ol>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        Project Demo<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        Project Demo<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        This project is aimed at early prediction of Parkinson\u2019s disease through computerized analysis of online handwriting samples. While a number of clinical evaluations are carried out to diagnose the Parkinson\u2019s disease, most of these tests are effective only once the disease is at a relatively advanced stage. Studies have shown that analysis of handwriting can […]<\/p>\n","protected":false},"author":1,"featured_media":4880,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[],"class_list":["post-4871","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-projects","entry","has-media"],"_links":{"self":[{"href":"https:\/\/coeai.bahria.edu.pk\/index.php\/wp-json\/wp\/v2\/posts\/4871","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/coeai.bahria.edu.pk\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/coeai.bahria.edu.pk\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/coeai.bahria.edu.pk\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/coeai.bahria.edu.pk\/index.php\/wp-json\/wp\/v2\/comments?post=4871"}],"version-history":[{"count":7,"href":"https:\/\/coeai.bahria.edu.pk\/index.php\/wp-json\/wp\/v2\/posts\/4871\/revisions"}],"predecessor-version":[{"id":5084,"href":"https:\/\/coeai.bahria.edu.pk\/index.php\/wp-json\/wp\/v2\/posts\/4871\/revisions\/5084"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/coeai.bahria.edu.pk\/index.php\/wp-json\/wp\/v2\/media\/4880"}],"wp:attachment":[{"href":"https:\/\/coeai.bahria.edu.pk\/index.php\/wp-json\/wp\/v2\/media?parent=4871"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/coeai.bahria.edu.pk\/index.php\/wp-json\/wp\/v2\/categories?post=4871"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/coeai.bahria.edu.pk\/index.php\/wp-json\/wp\/v2\/tags?post=4871"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}